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README.md
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tags:
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- generated_from_keras_callback
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model-index:
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- name: adult-content-
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results: []
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# adult-content-identify-text
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It achieves the following results on the evaluation set:
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- optimizer: None
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- training_precision: float32
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### Training results
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### Framework versions
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- Transformers 4.37.1
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- TensorFlow 2.15.0
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- Datasets 2.17.0
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- Tokenizers 0.15.1
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tags:
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- generated_from_keras_callback
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model-index:
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- name: adult-content-classifier-text
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results: []
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pipeline_tag: text-classification
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widget:
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- text: "情趣睡衣 性感睡衣 角色扮演 惹火 緊身連身 漆皮膠衣 黑 L"
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example_title: "adult_成人商品標題"
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- text: "男平織運動外套-立領外套 慢跑 路跑 藍黑螢光黃 L"
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example_title: "regular_一般商品標題"
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---
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<!-- This model card has been generated automatically according to the information Keras had access to. You should
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probably proofread and complete it, then remove this comment. -->
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# adult-content-identify-text
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Determine whether online sales products are adult content. Input: product title or description, Output results: 0 Unknown, 1 Adult Content, 2 General Merchandise.
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判斷網路銷售商品是否屬於成人內容。輸入: 商品名稱文字,輸出結果: 0 未知, 1 成人內容, 2 一般商品。
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# use transformers pipeline
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```python
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from transformers import pipeline
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pipe = pipeline("text-classification", model="jiechau/adult-content-classifier")
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#q = '男平織運動外套-立領外套 慢跑 路跑 藍黑螢光黃 L'
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q = '情趣睡衣 性感睡衣 角色扮演 惹火 緊身連身 漆皮膠衣 黑 L'
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result = pipe(q)
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print(result)
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# [{'label': 'adult_成人商品', 'score': 0.9994813799858093}]
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```
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### Framework versions
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- Transformers 4.37.1
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- TensorFlow 2.15.0
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- Datasets 2.17.0
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- Tokenizers 0.15.1
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